A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep density methods improve filtering in high-dimensional systems.
Robust Kalman filtering method for outlier detection.
Kernel learning FBSDE filter improves nonlinear filtering efficiency.
Study examines how COVID-19 affects bond yields using network filtering methods.
Advances deep network embedding through multi-filtering GCN.
The sophisticated structure of Convolutional Neural Network (CNN) allows for outstanding performance, but at the cost of intensive computation. As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction. Although ex…
We examine some differential geometric approaches to finding approximate solutions to the continuous time nonlinear filtering problem. Our primary focus is a new projection method for the optimal filter infinite dimensional Stochastic Partial Differential Equation (SPDE), based on the direct L2 metric and on a family o…
New deep learning method approximates Benes filter model.
A deep learning method solves nonlinear filtering problems efficiently.
Develops an inverse particle filter for cognitive systems.
New method improves nonlinear filtering accuracy with reduced computation.
In this paper we introduce a projection method for the space of probability distributions based on the differential geometric approach to statistics. This method is based on a direct L2 metric as opposed to the usual Hellinger distance and the related Fisher Information metric. We explain how this apparatus can be used…
A new method improves Bayesian filtering in nonlinear systems.
New method filters large networks from financial data to reveal key subnetworks.
EnSF improves accuracy in tracking high-dimensional nonlinear systems.
Gradient filters track moving parameters under noisy data and misspecification.
The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian Lévy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian Lévy noise may have infinite variance. A modified Kalman filter for linear systems wi…
We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and …
Novel filter uses deep BSDE for nonlinear density approximation.
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and cons…
This paper focuses on spectral filters on graphs, namely filters defined as elementwise multiplication in the frequency domain of a graph. In many graph signal processing settings, it is important to transfer a filter from one graph to another. One example is in graph convolutional neural networks (ConvNets), where the…
DNNs improve SIMP method but not spatially invariant, study shows.
This paper presents a fast and robust algorithm for trend filtering, a recently developed nonparametric regression tool. It has been shown that, for estimating functions whose derivatives are of bounded variation, trend filtering achieves the minimax optimal error rate, while other popular methods like smoothing spline…
A novel method reduces dimensionality for filtering SRNs with observed variables.
A new flow-based Bayesian filter tackles high-dimensional nonlinear stochastic systems.
A new ensemble filter uses transport maps and MMD optimization for high-dimensional data assimilation.
Adaptive Heston model calibration using PCRLB and switching filters.
Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters,, with the following outcomes: (1) we clarify what guarantees can and cannot be as…
The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. Like all nonparametric methods, the HP filter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these…
The parameters of temporal models, such as dynamic Bayesian networks, may be modelled in a Bayesian context as static or atemporal variables that influence transition probabilities at every time step. Particle filters fail for models that include such variables, while methods that use Gibbs sampling of parameter variab…
A new method reduces high-dimensional filtering to quadratic complexity.
Novel method uses Bayesian filters and PCRLB for state estimation of option prices.
This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part. Our method does not require additional annotations of object parts or textures for supervi…
FGD reduces noisy gradient variance in SGD for neural networks.
Develops Bayesian filtering for online learning and related problems.
Online convex optimization is a sequential prediction framework with the goal to track and adapt to the environment through evaluating proper convex loss functions. We study efficient particle filtering methods from the perspective of such a framework. We formulate an efficient particle filtering methods for the non-st…
We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative to conventional methods by combining rejection sampling with particle filtering. We also provide an approximate form of rejection sampling …
Improved state estimation in high-dimensional models using Zig-Zag Sampler.
Illustrates interleaved learning with Kalman Filter for linear least squares.
Enhanced ensemble filters use machine learning to improve accuracy in filtering models.
New method combines ODE filters and numerical quadrature to propagate model uncertainty.
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. We provide a theoretical bound to support our aggregation scheme, its numerical performa…
Filtering is a general name for inferring the states of a dynamical system given observations. The most common filtering approach is Gaussian Filtering (GF) where the distribution of the inferred states is a Gaussian whose mean is an affine function of the observations. There are two restrictions in this model: Gaussia…
ED-Filter improves eating disorder classification on Twitter.
This work preserves linear invariants in ensemble filters for non-Gaussian data assimilation.
Combines neural networks with splitting-up method for filtering equations.
A new method for Gaussian filtering using gradient flows and Wasserstein metrics.